| # BERT Fine-Tuning for IMDb Sentiment Classification |
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| A fine-tuned **BERT Base Uncased** model for **binary sentiment classification** on the IMDb Movie Reviews dataset. |
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| This project demonstrates the complete fine-tuning workflow using the Hugging Face ecosystem, from dataset preprocessing and tokenization to model training, evaluation, inference, and deployment. |
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| ## Model Details |
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| * **Base Model:** `bert-base-uncased` |
| * **Task:** Binary Sentiment Classification |
| * **Dataset:** IMDb Movie Reviews |
| * **Framework:** Hugging Face Transformers |
| * **Training Framework:** Trainer API |
| * **Language:** English |
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| --- |
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| ## Training Pipeline |
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| The model was trained using the following workflow: |
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| * Dataset loading using Hugging Face Datasets |
| * Tokenization with `AutoTokenizer` |
| * Fine-tuning using `AutoModelForSequenceClassification` |
| * Evaluation with Accuracy metric |
| * Mixed precision (FP16) training when CUDA is available |
| * Model exported using SafeTensors |
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| --- |
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| ## Performance |
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| The fine-tuned model learns to classify movie reviews into: |
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| * **LABEL_0 → Negative** |
| * **LABEL_1 → Positive** |
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| --- |
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| ## Usage |
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| ```python |
| from transformers import pipeline |
| |
| classifier = pipeline( |
| "text-classification", |
| model="YOUR_USERNAME/BERT-Fine-Tuning" |
| ) |
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| classifier("This movie was absolutely amazing!") |
| ``` |
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| Example output: |
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| ```python |
| [ |
| { |
| "label": "LABEL_1", |
| "score": 0.998 |
| } |
| ] |
| ``` |
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| --- |
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| ## Repository Contents |
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| * Fine-tuned model weights |
| * Tokenizer files |
| * Configuration files |
| * SafeTensors checkpoint |
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| The complete training notebook, source code, and documentation are available in the accompanying GitHub repository. |
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| --- |
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| ## Future Improvements |
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| * LoRA / PEFT fine-tuning |
| * Multi-class sentiment classification |
| * Hyperparameter optimization |
| * Model quantization |
| * ONNX and TensorRT deployment |
| * Production inference benchmarking |
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| --- |
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| ## License |
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| This project is released for educational and research purposes. |
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| --- |
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| Built with ❤️ by the author. |
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